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Development

Learn how to build real-time analytics applications and integrate Druid with ecosystem tools and libraries.

Four Key Considerations for Customer-Facing Analytics

Analytics aren’t just for internal stakeholders anymore. If you’re building an analytics application for customers, then you’re probably wondering…what’s the right database backend?

What Developers Can Build with Apache Druid

Exploring the popular uses for Apache Druid, a high-performance, real-time analytics database, and how it could fit in your data stack.

How to Connect Pivot to Multiple Druid Clusters

Learn how you can connect Pivot to several Imply Druid clusters, in order to visualize real-time data and build a more complete understanding of your performance and environment.

Embedding Visualizations with React and Express

This tutorial provides a step-by-step guide, with example, on integrating Imply Pivot’s embedding feature for secure pivot visualization in your React and Express application.

How to Build a Sentiment Analysis Application with ChatGPT and Druid

This blog demonstrates integrating ChatGPT and Apache Druid for sentiment analysis on tweets about ChatGPT. By leveraging AI and real-time analytics, businesses can make data-driven decisions, monitor brands, and analyze customer feedback efficiently.

Real-Time Analytics with Imply Polaris: From Setup to Visualization

This tutorial details how to setup an Imply account, create a datasource, and ingest data into Imply Polaris. It also covers how to use the console to query and visualize data.

Visualizing Data in Apache Druid with the Plotly Python Library

This tutorial details how to visualize data in Apache Druid with Plotly. It covers the process of querying data in Druid using SQL and creating visualizations that are rendered in a Flask web application.

Analyzing Clickstream Data

Discussion with Helmar Becker regarding clickstream data structure, delivery, ingestion and analytic query use cases.

Imply Polaris Quick Start Demo

This video walks through the process of creating a Polaris account, adding a data source, running queries, creating cubes, and visualizing data visualize data.

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